Mental imagery in interpreting – a neurocognitive perspective

The paper discusses mental imagery as an important part of information processing performed during interpreting. Mental imagery is examined to see if visual processing used to remember the source text or to facilitate its understanding helps to ‗off-load‘ other cognitive (mainly linguistic) resources in interpreting. The discussion is based on a neurocognitively-oriented depictivist model by Kosslyn (1994). The overview of mental imagery processes and systems is followed by the discussion of imagery used in interpreting. First, imagery development in student interpreters is described on the basis of a note-taking course for would-be consecutive interpreters organized by the author at AMU. The initial part of the course devoted to imagery involves visualizations of geographical, descriptive and narrative texts. The description abounds in authentic examples and presents conclusions for interpreting trainers. Later, imagery as employed by professional interpreters is discussed on the basis of a qualitative survey. General implications of the use of mental imagery for cognitive processing limitations in interpreting are presented in the concluding section.

Probabilistic Egomotion for Stereo Visual Odometry

We present a novel approach of Stereo Visual Odometry for vehicles equipped with calibrated stereo cameras. We combine a dense probabilistic 5D egomotion estimation method with a sparse keypoint based stereo approach to provide high quality estimates of vehicle’s angular and linear velocities. To validate our approach, we perform two sets of experiments with a well known benchmarking dataset. First, we assess the quality of the raw velocity estimates in comparison to classical pose estimation algorithms. Second, we added to our method’s instantaneous velocity estimates a Kalman Filter and compare its performance with a well known open source stereo Visual Odometry library. The presented results compare favorably with state-of-the-art approaches, mainly in the estimation of the angular velocities, where significant improvements are achieved.

Probabilistic Stereo Egomotion Transform

In this paper we propose a novel fully probabilistic solution to the stereo egomotion estimation problem. We extend the notion of probabilistic correspondence to the stereo case which allow us to compute the whole 6D motion information in a probabilistic way. We compare the developed approach against other known state-of-the-art methods for stereo egomotion estimation, and the obtained results compare favorably both for the linear and angular velocities estimation.